Research on Distributed Photovoltaic Autonomous Optimization Control Strategy Under the Guidance of Voltage Optimization Principle

This paper innovatively describes a decentralized light energy self-regulation strategy rooted in the voltage optimization law. The scheme comprehensively considers the transient characteristics and decentralized pattern of light conversion, integrates GA (genetic algorithm) and PSO (particle swarm optimization) to build a hybrid optimization framework, and is committed to achieving the optimal regulation of photovoltaic production capacity based on ensuring the steady state of the power storage architecture. The first step is to conceive a voltage optimization blueprint specifically for decentralized light energy, whose function is to flexibly adjust the photovoltaic power consumption quota according to the real-time operating conditions of the power distribution network. With the help of the composite algorithm of GA superimposed on PSO, the cyclic refinement of the self-regulation strategy is realized. The results of simulation experiments show that this method can effectively reduce the degree of voltage fluctuation, enhance the robustness of photovoltaic equipment connected to the power grid, and simultaneously increase the overall efficiency of the system. Detailed numerical comparison shows that after adopting this method, the system voltage deviation from the standard value is reduced by as much as 12.3%, and the light-to-electricity efficiency ratio is increased to 8.6%. Compared with the previous control methods, the strategy presented in this paper has obvious advantages in voltage improvement and energy utilization efficiency. This study provides key insights for the optimized control of distributed solar energy connected to the grid.